In the art of video analytics, video frames are analyzed and compared in order to detect objects or events therein. Many researchers are working on different methods of extracting objects and events from video data. For example, extracting an object and tracking its motion is currently heavily studied. Another area that has found considerable interest is face extraction and analysis. As analysis methodologies improve, they become more effective. In time, they will likely gain in efficiency as well. That said, analysis requires considerable processing power and is therefore most commonly performed on a dedicated high performance system.
Another area garnering considerable attention is massively parallel processing. In massively parallel processing a same problem is distributed amongst a very large number of computer systems. For example, to break an encrypted message, different keys are provided to each of a large number of systems to “test” on the message. When a suitable decrypted message results it is passed to another system for review. As such, instead of serializing a brute force attack, the attack is highly parallelized. With 100,000,000 computers it is possible to test 100,000,000 keys at once being approximately 26 bits. Thus, a 40 bit key is reduced to the efficacy of a 14 bit key. As is evident from this example, there is considerable power in numbers especially for easily parallelized operations.
Another reason massively parallel computing is of interest is because most computer systems today are idle much of the time. Using up an idle resource is better than acquiring more resources for a same problem.
Unfortunately, massively parallel processing is ill suited to video analytics, not for technical reasons, but for privacy reasons. People do not want their video data to be publicly available as it would be in a massively parallel approach to video analytics. For example, a processor that is idle could analyze movement in a home based on video data, but that would allow that system, and potentially others, to view the hallway and what happens there. This is unlikely to be acceptable to everyone.
It would be advantageous to provide a method of video analytics that retains privacy of the video data and that is suitable to massively parallel application or to distributed processing on peer computers.